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Bridging the Semantic Gap in Image and Video Analysis [electronic resource] / edited by Halina Kwaśnicka, Lakhmi C. Jain.

Contributor(s): Kwaśnicka, Halina [editor.] | Jain, Lakhmi C [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Intelligent Systems Reference Library: 145Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: X, 163 p. 59 illus., 48 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319738918.Subject(s): Computational intelligence | Semiotics | Artificial intelligence | Signal processing | Computer vision | Computational Intelligence | Semiotics | Artificial Intelligence | Signal, Speech and Image Processing | Computer VisionAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Semantic Gap in Image and Video Analysis: An Introduction -- Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study -- Scale-insensitive MSER Features: A Promising Tool for Meaningful Segmentation of Images -- Active Partitions in Localization of Semantically Important Image Structures -- Model-based 3D Object recognition in RGB-D Images -- Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning -- Deep Learning – a New Era in Bridging the Semantic Gap.
In: Springer Nature eBookSummary: This book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image. The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels. The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on.
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Semantic Gap in Image and Video Analysis: An Introduction -- Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study -- Scale-insensitive MSER Features: A Promising Tool for Meaningful Segmentation of Images -- Active Partitions in Localization of Semantically Important Image Structures -- Model-based 3D Object recognition in RGB-D Images -- Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning -- Deep Learning – a New Era in Bridging the Semantic Gap.

This book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image. The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels. The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on.

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